feat: fixed train.py and predictions.py
This commit is contained in:
Jakub Zaręba
parent
4a7ae9e7bc
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34
ium_DL.py
34
ium_DL.py
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import tensorflow as tf
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import pandas as pd
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train_data = pd.read_csv('olympics-124-years-datasettill-2020/Athletes_winter_games.csv')
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X_train = train_data[['Sex']]
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y_train = train_data['Medal']
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X_train.loc[:, 'Sex'] = X_train['Sex'].map({'M': 0, 'F': 1})
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y_train = y_train.map({'Bronze': 0, 'Silver': 1, 'Gold': 1}).fillna(0).astype('float32')
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X_train = X_train.astype('float32')
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y_train = y_train.astype('float32')
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model = tf.keras.Sequential([
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tf.keras.layers.Dense(16, activation='relu', input_shape=(X_train.shape[1],)),
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tf.keras.layers.Dense(1, activation='sigmoid')
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])
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model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
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model.fit(X_train, y_train, epochs=10)
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model.save('model.h5')
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test_data = pd.read_csv('olympics-124-years-datasettill-2020/Athletes_winter_games.csv')
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test_data.loc[:, 'Sex'] = test_data['Sex'].map({'M': 0, 'F': 1})
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test_data = test_data[['Sex']].astype('float32')
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predictions = model.predict(test_data)
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pd.DataFrame(predictions).to_csv('predictions.csv', index=False, header=False)
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144
lab5.ipynb
144
lab5.ipynb
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 51,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Epoch 1/10\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"C:\\Users\\kmjay\\AppData\\Local\\Temp\\ipykernel_17164\\3575846689.py:9: SettingWithCopyWarning: \n",
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"A value is trying to be set on a copy of a slice from a DataFrame.\n",
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"Try using .loc[row_indexer,col_indexer] = value instead\n",
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"\n",
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"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
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" X_train.loc[:, 'Sex'] = X_train['Sex'].map({'M': 0, 'F': 1})\n",
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"C:\\Users\\kmjay\\AppData\\Local\\Temp\\ipykernel_17164\\3575846689.py:9: DeprecationWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`\n",
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" X_train.loc[:, 'Sex'] = X_train['Sex'].map({'M': 0, 'F': 1})\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"1518/1518 [==============================] - 2s 758us/step - loss: 0.3609 - accuracy: 0.9112\n",
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"Epoch 2/10\n",
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"1518/1518 [==============================] - 1s 726us/step - loss: 0.2763 - accuracy: 0.9216\n",
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"Epoch 3/10\n",
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"1518/1518 [==============================] - 1s 731us/step - loss: 0.2751 - accuracy: 0.9216\n",
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"Epoch 4/10\n",
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"1518/1518 [==============================] - 1s 725us/step - loss: 0.2750 - accuracy: 0.9216\n",
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"Epoch 5/10\n",
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"1518/1518 [==============================] - 1s 733us/step - loss: 0.2750 - accuracy: 0.9216\n",
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"Epoch 6/10\n",
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"1518/1518 [==============================] - 1s 733us/step - loss: 0.2750 - accuracy: 0.9216\n",
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"Epoch 7/10\n",
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"1518/1518 [==============================] - 1s 729us/step - loss: 0.2750 - accuracy: 0.9216\n",
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"Epoch 8/10\n",
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"1518/1518 [==============================] - 1s 728us/step - loss: 0.2750 - accuracy: 0.9216\n",
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"Epoch 9/10\n",
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"1518/1518 [==============================] - 1s 727us/step - loss: 0.2750 - accuracy: 0.9216\n",
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"Epoch 10/10\n",
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"1518/1518 [==============================] - 1s 755us/step - loss: 0.2750 - accuracy: 0.9216\n"
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]
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}
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],
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"source": [
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"import tensorflow as tf\n",
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"import pandas as pd\n",
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"\n",
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"train_data = pd.read_csv('olympics-124-years-datasettill-2020/Athletes_winter_games.csv')\n",
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"\n",
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"X_train = train_data[['Sex']]\n",
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"y_train = train_data['Medal']\n",
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"\n",
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"X_train.loc[:, 'Sex'] = X_train['Sex'].map({'M': 0, 'F': 1})\n",
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"y_train = y_train.map({'Bronze': 0, 'Silver': 1, 'Gold': 1}).fillna(0).astype('float32')\n",
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"\n",
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"X_train = X_train.astype('float32')\n",
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"y_train = y_train.astype('float32')\n",
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"\n",
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"model = tf.keras.Sequential([\n",
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" tf.keras.layers.Dense(16, activation='relu', input_shape=(X_train.shape[1],)),\n",
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" tf.keras.layers.Dense(1, activation='sigmoid')\n",
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"])\n",
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"\n",
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"model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
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"\n",
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"model.fit(X_train, y_train, epochs=10)\n",
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"\n",
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"model.save('model.h5')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 52,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" 170/1518 [==>...........................] - ETA: 0s"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"C:\\Users\\kmjay\\AppData\\Local\\Temp\\ipykernel_17164\\2746302769.py:3: DeprecationWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`\n",
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" test_data.loc[:, 'Sex'] = test_data['Sex'].map({'M': 0, 'F': 1})\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"1518/1518 [==============================] - 1s 574us/step\n"
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]
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}
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],
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"source": [
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"test_data = pd.read_csv('olympics-124-years-datasettill-2020/Athletes_winter_games.csv')\n",
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"\n",
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"test_data.loc[:, 'Sex'] = test_data['Sex'].map({'M': 0, 'F': 1})\n",
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"test_data = test_data[['Sex']].astype('float32')\n",
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"\n",
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"predictions = model.predict(test_data)\n",
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"\n",
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"pd.DataFrame(predictions).to_csv('predictions.csv', index=False, header=False)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.3"
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},
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"orig_nbformat": 4
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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97129
predictions.csv
97129
predictions.csv
File diff suppressed because it is too large
Load Diff
19
predictions.py
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predictions.py
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import pandas as pd
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import tensorflow as tf
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from sklearn.preprocessing import MinMaxScaler
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model = tf.keras.models.load_model('model.h5')
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data = pd.read_csv('data.csv', sep=';')
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data = pd.get_dummies(data, columns=['Sex', 'Medal'])
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data = data.drop(columns=['Name', 'Team', 'NOC', 'Games', 'Year', 'Season', 'City', 'Sport', 'Event'])
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scaler = MinMaxScaler()
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data = pd.DataFrame(scaler.fit_transform(data), columns=data.columns)
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X_test = data.filter(regex='Sex|Age')
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predictions = model.predict(X_test)
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pd.DataFrame(predictions).to_csv('predictions.csv', index=False)
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train.py
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train.py
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import pandas as pd
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from sklearn.model_selection import train_test_split
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from sklearn.preprocessing import MinMaxScaler
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import tensorflow as tf
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from imblearn.over_sampling import SMOTE
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smote = SMOTE(random_state=42)
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data = pd.read_csv('data.csv', sep=';')
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print('Total rows:', len(data))
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print('Rows with medal:', len(data.dropna(subset=['Medal'])))
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data = pd.get_dummies(data, columns=['Sex', 'Medal'])
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data = data.drop(columns=['Name', 'Team', 'NOC', 'Games', 'Year', 'Season', 'City', 'Sport', 'Event'])
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scaler = MinMaxScaler()
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data = pd.DataFrame(scaler.fit_transform(data), columns=data.columns)
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X = data.filter(regex='Sex|Age')
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y = data.filter(regex='Medal')
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y = pd.get_dummies(y)
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X = X.fillna(0)
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y = y.fillna(0)
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y = y.values
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X_resampled, y_resampled = smote.fit_resample(X, y)
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X_train, X_test, y_train, y_test = train_test_split(X_resampled, y_resampled, test_size=0.2, random_state=42)
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model = tf.keras.models.Sequential()
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model.add(tf.keras.layers.Dense(64, input_dim=X_train.shape[1], activation='relu'))
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model.add(tf.keras.layers.Dense(32, activation='relu'))
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model.add(tf.keras.layers.Dense(y.shape[1], activation='softmax'))
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model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
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model.fit(X_train, y_train, epochs=10, batch_size=32)
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loss, accuracy = model.evaluate(X_test, y_test)
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print('Test accuracy:', accuracy)
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model.save('model.h5')
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